Generalisation of deep neural networks becomes vulnerable when distribution shifts are encountered between train (source) and test (target) domain data. Few-shot domain adaptation mitigates this issue by adapting deep neural networks pre-trained on the source domain to the target domain using a randomly selected and annotated support set from the target domain. This paper argues that randomly selecting the support set can be further improved for effectively adapting the pre-trained source models to the target domain. Alternatively, we propose SelectNAdapt, an algorithm to curate the selection of the target domain samples, which are then annotated and included in the support set. In particular, for the K-shot adaptation problem, we first leverage self-supervision to learn features of the target domain data. Then, we propose a per-class clustering scheme of the learned target domain features and select K representative target samples using a distance-based scoring function. Finally, we bring our selection setup towards a practical ground by relying on pseudo-labels for clustering semantically similar target domain samples. Our experiments show promising results on three few-shot domain adaptation benchmarks for image recognition compared to related approaches and the standard random selection.

翻译：摘要：当训练（源域）与测试（目标域）数据产生分布偏移时，深度神经网络的泛化能力会变得脆弱。少样本域自适应通过从目标域中随机选取并标注支持集，对在源域上预训练的深度神经网络进行微调，以缓解该问题。本文指出，为有效将预训练源模型适配至目标域，随机选择支持集的方式仍有改进空间。为此，我们提出SelectNAdapt算法，通过精选目标域样本并将其标注后纳入支持集。具体而言，针对K-shot自适应问题，首先利用自监督学习提取目标域数据特征，随后提出基于类别的特征聚类方案，并通过距离评分函数筛选出K个代表性目标样本。最后，通过依赖伪标签对语义相似的目标域样本进行聚类，使所提选择方法更贴近实际应用场景。实验表明，在三个少样本域自适应图像识别基准上，相较相关方法及标准随机选择策略，本方法取得了令人瞩目的性能提升。